Persistence, Randomization, and Spatial Noise (revised paper)

Morgan Kelly

No 202125, Working Papers from School of Economics, University College Dublin

Abstract: Historical persistence studies and other regressions using spatial data commonly return severely inflated t statistics, and different standard error estimates that attempt to correct for this vary so widely as to be as to be of limited use in practice. This paper proposes a simple randomization inference procedure where the significance level of an explanatory variable is measured by its ability to outperform synthetic noise with the same estimated spatial structure. Spatial noise, in other words, acts as a treatment randomization in an artificial experiment based on correlated observational data. Examining twenty persistence studies, few perform substantially above the level of spatial noise.

Keywords: Spatial noise; Standard error; Randomization inference; Exchangeable observations; HIstorical persistence (search for similar items in EconPapers)
JEL-codes: N0 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2021-11
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10197/12770 First version, 2021 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ucn:wpaper:202125

Access Statistics for this paper

More papers in Working Papers from School of Economics, University College Dublin Contact information at EDIRC.
Bibliographic data for series maintained by UCD School of Economics ().